Exp03_VAE_to_Malimg.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Exp03. VAE to Malimg.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMqdiIQR3gBcw9/0s58mZSF",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Steve-YJ/deep-learning-from-scratch-studying/blob/master/Exp03_VAE_to_Malimg.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YTIFa5F2GXqh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 146
        },
        "outputId": "165a2a4b-c470-4dfb-dd83-8fd8a461ce4a"
      },
      "source": [
        "# copy from Exp02. run_GAN\n",
        "\n",
        "# drive mount\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive', force_remount=True)\n",
        "\n",
        "FOLDERNAME = '/Malimg_Exp_200611'\n",
        "\n",
        "assert FOLDERNAME is not None, \"[!] Enter the foldername.\"\n",
        "\n",
        "%cd drive/My\\ Drive\n",
        "# %cp -r $FOLDERNAME ../../\n",
        "# %cd ../../\n",
        "# %cd /Malimg_Exp_200611/\n",
        "# !bash get_datasets.sh\n",
        "# %cd ../../"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n",
            "/content/drive/My Drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-i54IefrHIyU",
        "colab_type": "text"
      },
      "source": [
        "## pickle 데이터 불러오기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KZrJyjQxHKqC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pickle\n",
        "\n",
        "with open('/content/drive/My Drive/Malimg_Exp_200611/malimg_data2.pkl', 'rb') as f:\n",
        "    data = pickle.load(f)"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mQ0RlctMG_pL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "1fdc4a9b-7a59-45a5-ea16-8b4c0110138a"
      },
      "source": [
        "! pwd"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/My Drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C_SFnWy0HdJJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "1b85bf40-559e-4aec-f1f5-3236bae19f52"
      },
      "source": [
        "%cd Malimg_Exp_200611"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/My Drive/Malimg_Exp_200611\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "54hpPKEfH6xi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "8b8b0267-dcc1-4461-ef38-280ccc157957"
      },
      "source": [
        "! pwd"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/My Drive/Malimg_Exp_200611\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jYOdhvAuH8Cw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 165
        },
        "outputId": "678f2b42-0fa5-4736-f40f-8c9b8e9d566d"
      },
      "source": [
        "! pip install -r requirements.txt"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: torch in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 1)) (1.5.1+cu101)\n",
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 2)) (0.6.1+cu101)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 3)) (4.41.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 4)) (1.12.0)\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from torch->-r requirements.txt (line 1)) (0.16.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from torch->-r requirements.txt (line 1)) (1.18.5)\n",
            "Requirement already satisfied: pillow>=4.1.1 in /usr/local/lib/python3.6/dist-packages (from torchvision->-r requirements.txt (line 2)) (7.0.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qc0503ZWJZWU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# ! python main.py\n",
        "\n",
        "# 잘 동작합니다"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IBTELiU8ME5c",
        "colab_type": "text"
      },
      "source": [
        "1st. 코드 파악하기<br>\n",
        "* MNIST 데이터로 짜여진 VAE main.py 파악하기\n",
        "* Encoder, Decoder, Loss Function 등 파악\n",
        "\n",
        "* Again, Review를 잘 한것 같았으나... 코드가 동작하지 않음!\n",
        "    * 아마 Custom DataLoader에 문제가 있지 않나 생각합니다..."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XqHTfWEjMpCf",
        "colab_type": "text"
      },
      "source": [
        "## #1. Data Preprocessing\n",
        "\n",
        "* Do-it\n",
        "    * Data 구조 파악하기\n",
        "    * Data Loader 만들기"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0wyU07zmTkBl",
        "colab_type": "text"
      },
      "source": [
        "### preprocessing1. numpy to tensor "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nnr49OB-J74g",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "617a7af7-e033-4ec6-efc4-8f3397f09281"
      },
      "source": [
        "data[0].shape, data[1].shape"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((9339, 50176), (9339,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u-ETRaGfMuXJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# type(data[0][0])\n",
        "# make numpy array to tensor"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f8c8FeUESKlf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "3b6c1376-43e9-4e9d-cfea-6e8f2bf92d9b"
      },
      "source": [
        "import torch\n",
        "\n",
        "# convert numpy to tensor\n",
        "train_X = torch.from_numpy(data[0])\n",
        "train_X.shape"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([9339, 50176])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nIciNh5ZTHXp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "58d94df0-69d0-4bdc-dcc2-9139f451aec3"
      },
      "source": [
        "train_X = train_X.reshape(-1, 1, 224, 224)\n",
        "train_X.shape"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([9339, 1, 224, 224])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FqzACAsNe44n",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "2b6144df-03f9-4f91-f94a-8dd707a52a18"
      },
      "source": [
        "# train_X.transfrom()"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-51-ca8385a23f19>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrain_X\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransfrom\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m: 'Tensor' object has no attribute 'transfrom'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QgpLRDlsTQMC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "f219daaf-b43b-4c91-d606-0352fc758d0f"
      },
      "source": [
        "# also convert y to tensor\n",
        "\n",
        "train_y = torch.from_numpy(data[1])\n",
        "train_y"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([ 0.,  0.,  0.,  ..., 24., 24., 24.], dtype=torch.float64)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9REskGCpTZp6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "outputId": "d09b40d1-403e-40f9-9dc8-d573c8f3aa44"
      },
      "source": [
        "train_y.shape, train_y.unique()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(torch.Size([9339]),\n",
              " tensor([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11., 12., 13.,\n",
              "         14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24.],\n",
              "        dtype=torch.float64))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s5_QBCPQTaxv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "c1782d74-3b72-4051-c911-d53532ca7e03"
      },
      "source": [
        "train_X.shape, train_y.shape"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(torch.Size([9339, 1, 224, 224]), torch.Size([9339]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eWv6FHbUU3M4",
        "colab_type": "text"
      },
      "source": [
        "### Train_Test_Split이 필요하지 않나요?!\n",
        "\n",
        "* Reconstruction을 하기 위해서는 test데이터가 필요\n",
        "* <a href='https://github.com/pytorch/examples/blob/master/vae/main.py'>참고한 git</a>에서는 train, test data 사용"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BeO_GaylVanG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "8c6de163-8782-430a-918a-4c9e7c4dc7a9"
      },
      "source": [
        "# train_test split\n",
        "# Reference: https://github.com/Steve-YJ/deep-learning-from-scratch-studying/blob/master/02_PyTorch_Introduction_Basic/Ch05_5.5_%EC%99%80%EC%9D%B8%20%EB%B6%84%EB%A5%98%ED%95%98%EA%B8%B0.ipynb\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "train_X, test_X, train_Y, test_Y = train_test_split(train_X, train_y, test_size=0.2)\n",
        "\n",
        "# 데이터 확인\n",
        "print(train_X.shape)\n",
        "print(test_X.shape)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "torch.Size([7471, 1, 224, 224])\n",
            "torch.Size([1868, 1, 224, 224])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nAgJIpO6e9vL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# train_X"
      ],
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VbO9AeIrToey",
        "colab_type": "text"
      },
      "source": [
        "### preprocessing 2. Make DataLoader\n",
        "\n",
        "* train, test 모두 dataloader를 만들어줘야 한다"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BffCP4ztTiXP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "b894a904-f860-4a2b-d051-5a679e415fcd"
      },
      "source": [
        "import torch\n",
        "import numpy as np\n",
        "from torch.autograd import Variable\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "\n",
        "%load_ext autoreload\n",
        "%autoreload 2"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The autoreload extension is already loaded. To reload it, use:\n",
            "  %reload_ext autoreload\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VCU4ocXST8up",
        "colab_type": "text"
      },
      "source": [
        "* Reference: \n",
        "    * Reference:\n",
        "PyTorch로 시작하는 딥러닝 입문: https://wikidocs.net/57165\n",
        "    * Dataset 클래스 정의\n",
        "    * Dataloader 만들기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vo2W25lkT6v8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class Malimg_train(torch.utils.data.Dataset):\n",
        "    \"\"\"Malimg dataset\"\"\"\n",
        "\n",
        "    # Initialize your data, \n",
        "    def __init__(self):\n",
        "        self.X_data = train_X\n",
        "        self.y_data = train_Y\n",
        "\n",
        "    def __getitem__(self, index):  ## torch.FlaotTensor()\n",
        "                                   #  https://wikidocs.net/57165\n",
        "        return torch.FloatTensor(self.X_data[index]), torch.FloatTensor(self.y_data[index])\n",
        "    \n",
        "    def __len__(self):\n",
        "        return len(self.X_data)"
      ],
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7qirGaWoWool",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class Malimg_test(torch.utils.data.Dataset):\n",
        "    \"\"\"Malimg dataset\"\"\"\n",
        "\n",
        "    # Initialize your data, \n",
        "    def __init__(self):\n",
        "        self.X_data = test_X\n",
        "        self.y_data = test_Y\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "        return self.X_data[index], self.y_data[index]\n",
        "    \n",
        "    def __len__(self):\n",
        "        return len(self.X_data)"
      ],
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iyUkSlp7W1x0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "## 클래스 인스턴스화\n",
        "## Reference: https://tutorials.pytorch.kr/beginner/data_loading_tutorial.html\n",
        "\n",
        "malimg_train = Malimg_train()\n",
        "malimg_test = Malimg_test()"
      ],
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4wcfYRdnWrad",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_loader = DataLoader(malimg_train)\n",
        "test_loader = DataLoader(malimg_test)"
      ],
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GKxGZq76m7VK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "cf98d483-7de8-4265-d9c4-8cdb9b4a5a0d"
      },
      "source": [
        "type(train_loader)"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.utils.data.dataloader.DataLoader"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XN6BpfozXhbc",
        "colab_type": "text"
      },
      "source": [
        "## #2. VAE 코드 작성하기\n",
        "\n",
        "* Reference: https://github.com/pytorch/examples/blob/master/vae/main.py"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eT-MO_4nXNgA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "2a46d5ce-461e-46af-93b8-850dc7798a34"
      },
      "source": [
        "from __future__ import print_function\n",
        "import argparse\n",
        "import torch\n",
        "import torch.utils.data\n",
        "from torch import nn, optim\n",
        "from torch.nn import functional as F\n",
        "from torchvision import datasets, transforms\n",
        "from torchvision.utils import save_image\n",
        "\n",
        "\n",
        "# parser = argparse.ArgumentParser(description='VAE MNIST Example')\n",
        "# parser.add_argument('--batch-size', type=int, default=32, metavar='N',\n",
        "#                     help='input batch size for training (default: 128)')\n",
        "# parser.add_argument('--epochs', type=int, default=20, metavar='N',\n",
        "#                     help='number of epochs to train (default: 10)')\n",
        "# parser.add_argument('--no-cuda', action='store_true', default=False,\n",
        "#                     help='enables CUDA training')\n",
        "# parser.add_argument('--seed', type=int, default=1, metavar='S',\n",
        "#                     help='random seed (default: 1)')\n",
        "# parser.add_argument('--log-interval', type=int, default=10, metavar='N',\n",
        "#                     help='how many batches to wait before logging training status')\n",
        "# args = parser.parse_args()\n",
        "# args.cuda = not args.no_cuda and torch.cuda.is_available()\n",
        "\n",
        "# torch.manual_seed(args.seed)\n",
        "\n",
        "device = torch.device(\"cuda\")\n",
        "\n",
        "# kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}\n",
        "# train_loader = torch.utils.data.DataLoader(\n",
        "#     datasets.MNIST('../data', train=True, download=True,\n",
        "#                    transform=transforms.ToTensor()),\n",
        "#     batch_size=args.batch_size, shuffle=True, **kwargs)\n",
        "# test_loader = torch.utils.data.DataLoader(\n",
        "#     datasets.MNIST('../data', train=False, transform=transforms.ToTensor()),\n",
        "#     batch_size=args.batch_size, shuffle=True, **kwargs)\n",
        "\n",
        "\n",
        "class VAE(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(VAE, self).__init__()\n",
        "\n",
        "        self.fc1 = nn.Linear(50176, 400)\n",
        "        self.fc21 = nn.Linear(400, 20)\n",
        "        self.fc22 = nn.Linear(400, 20)\n",
        "        self.fc3 = nn.Linear(20, 400)\n",
        "        self.fc4 = nn.Linear(400, 50176)\n",
        "\n",
        "    def encode(self, x):\n",
        "        h1 = F.relu(self.fc1(x))\n",
        "        return self.fc21(h1), self.fc22(h1)\n",
        "\n",
        "    def reparameterize(self, mu, logvar):\n",
        "        std = torch.exp(0.5*logvar)\n",
        "        eps = torch.randn_like(std)\n",
        "        return mu + eps*std\n",
        "\n",
        "    def decode(self, z):\n",
        "        h3 = F.relu(self.fc3(z))\n",
        "        return torch.sigmoid(self.fc4(h3))\n",
        "\n",
        "    def forward(self, x):\n",
        "        mu, logvar = self.encode(x.view(-1, 50176))\n",
        "        z = self.reparameterize(mu, logvar)\n",
        "        return self.decode(z), mu, logvar\n",
        "\n",
        "\n",
        "model = VAE().to(device)\n",
        "optimizer = optim.Adam(model.parameters(), lr=1e-3)\n",
        "\n",
        "\n",
        "# Reconstruction + KL divergence losses summed over all elements and batch\n",
        "def loss_function(recon_x, x, mu, logvar):\n",
        "    BCE = F.binary_cross_entropy(recon_x, x.view(-1, 50176), reduction='sum')\n",
        "\n",
        "    # see Appendix B from VAE paper:\n",
        "    # Kingma and Welling. Auto-Encoding Variational Bayes. ICLR, 2014\n",
        "    # https://arxiv.org/abs/1312.6114\n",
        "    # 0.5 * sum(1 + log(sigma^2) - mu^2 - sigma^2)\n",
        "    KLD = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())\n",
        "\n",
        "    return BCE + KLD\n",
        "\n",
        "\n",
        "def train(epoch):\n",
        "    model.train()\n",
        "    train_loss = 0\n",
        "    for batch_idx, (data, _) in enumerate(train_loader):\n",
        "        data = data.to(device)\n",
        "        optimizer.zero_grad()\n",
        "        recon_batch, mu, logvar = model(data)\n",
        "        loss = loss_function(recon_batch, data, mu, logvar)\n",
        "        loss.backward()\n",
        "        train_loss += loss.item()\n",
        "        optimizer.step()\n",
        "        if batch_idx % 10 == 0:\n",
        "            print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n",
        "                epoch, batch_idx * len(data), len(train_loader.dataset),\n",
        "                100. * batch_idx / len(train_loader),\n",
        "                loss.item() / len(data)))\n",
        "\n",
        "    print('====> Epoch: {} Average loss: {:.4f}'.format(\n",
        "          epoch, train_loss / len(train_loader.dataset)))\n",
        "\n",
        "\n",
        "def test(epoch):\n",
        "    model.eval()\n",
        "    test_loss = 0\n",
        "    with torch.no_grad():\n",
        "        for i, (data, _) in enumerate(test_loader):\n",
        "            data = data.to(device)\n",
        "            recon_batch, mu, logvar = model(data)\n",
        "            test_loss += loss_function(recon_batch, data, mu, logvar).item()\n",
        "            if i == 0:\n",
        "                n = min(data.size(0), 8)\n",
        "                comparison = torch.cat([data[:n],\n",
        "                                      recon_batch.view(32, 1, 224, 224)[:n]])\n",
        "                save_image(comparison.cpu(),\n",
        "                         'results/reconstruction_' + str(epoch) + '.png', nrow=n)\n",
        "\n",
        "    test_loss /= len(test_loader.dataset)\n",
        "    print('====> Test set loss: {:.4f}'.format(test_loss))\n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "    for epoch in range(1, 20 + 1):\n",
        "        train(epoch)\n",
        "        test(epoch)\n",
        "\n",
        "        with torch.no_grad():\n",
        "            sample = torch.randn(64, 20).to(device)\n",
        "            sample = model.decode(sample).cpu()\n",
        "            save_image(sample.view(64, 1, 224, 224),\n",
        "                       'results/sample_' + str(epoch) + '.png')"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-60-166c48bb2bec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m    124\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0m__name__\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"__main__\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    125\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mepoch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m20\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 126\u001b[0;31m         \u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepoch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    127\u001b[0m         \u001b[0mtest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepoch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    128\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-60-166c48bb2bec>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(epoch)\u001b[0m\n\u001b[1;32m     86\u001b[0m     \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     87\u001b[0m     \u001b[0mtrain_loss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 88\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0mbatch_idx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_loader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     89\u001b[0m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     90\u001b[0m         \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzero_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    343\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    344\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__next__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 345\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    346\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_yielded\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    347\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_kind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0m_DatasetKind\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIterable\u001b[0m \u001b[0;32mand\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    383\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    384\u001b[0m         \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 385\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_fetcher\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    386\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pin_memory\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    387\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/fetch.py\u001b[0m in \u001b[0;36mfetch\u001b[0;34m(self, possibly_batched_index)\u001b[0m\n\u001b[1;32m     42\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_collation\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 44\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     45\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     46\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/fetch.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     42\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_collation\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 44\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     45\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     46\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-55-ec42f3487053>\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, index)\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m## torch.FlaotTensor()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m                                    \u001b[0;31m#  https://wikidocs.net/57165\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFloatTensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mX_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFloatTensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0my_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: expected Float (got Double)"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZbOebd-zapfF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}